薬理学における人工知能による形成的評価に関する学生の見解
Jon Andsnes Berg1,2, Øyvind Repstad3, Trond Trætteberg Serkland1,2
1Department of Clinical Science, Faculty of Medicine, University of Bergen, Bergen, Norway.
British journal of clinical pharmacology
|August 30, 2025
まとめ
医学生たちは,人工知能 (AI) のフィードバックが薬理学学習に有用であると認めましたが,そのポジティブ性と評価デザインに関する懸念を指摘しました. 効果的なAIフィードバックには 学生のフィードバック・リテラシーと プログラムサポートの訓練が必要です
科学分野:
- 医学教育
- 教育における人工知能
- 薬学教育
背景:
- 医学生の薬学学習には 質の高いフィードバックが不可欠です
- パーソナライズされたフィードバックは 資源密集的で AI ソリューションの探索を促します
- AIによるフィードバックに関する学生の見解は ほとんど不明です
研究 の 目的:
- 医学生の知覚とAIによるフィードバックの活用を,形成的な薬理学評価で調査する.
- 学生のAIフィードバックの経験に影響を与える要因を理解する.
主な方法:
- 3年生の医学生を対象とした定性研究
- 8つの短解の薬理学質問による形成的評価
- フォーカスグループ (n=11) は,繰り返しテーマ分析を用いて分析した.
主要な成果:
- 3つの主要なテーマが浮上した. フィードバックの信頼性/アクセシビリティ, 学習プログラムとの連携, 学生のフィードバックの読み書き能力.
- 学生はAIのフィードバックを有用で 大抵は信頼できるものだと捉えました
- AIのポジティブ性,評価のタイミング/委任,長時間のフィードバックによる作業量などが懸念された.
結論:
- AIによる形成的評価の有効性は,技術的,文脈的,個々の要因に依存しています.
- 人工知能のフィードバックの成功には 学生にフィードバックの能力を与える必要があります
- 教育プログラムはAIによって生み出されるフィードバックに 意味のある関わり方を奨励しなければなりません
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